{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# One step univariate feed-forward neural network model\n",
    "\n",
    "In this notebook, we demonstrate how to:\n",
    "- prepare time series data for training a feed-forward neural network (NN) forecasting model\n",
    "- get data in the required shape for the keras API\n",
    "- implement a simple feed-forward NN model in keras to predict the next step ahead (time *t+1*) in the time series\n",
    "- enable early stopping to reduce the likelihood of model overfitting\n",
    "- evaluate the model on a test dataset\n",
    "\n",
    "The data in this example is taken from the GEFCom2014 forecasting competition<sup>1</sup>. It consists of 3 years of hourly electricity load and temperature values between 2012 and 2014. The task is to forecast future values of electricity load. In this example, we show how to forecast one time step ahead, using historical load data only.\n",
    "\n",
    "<sup>1</sup>Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, \"Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond\", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please run this notebook after completing 0_data_setup notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import datetime as dt\n",
    "from glob import glob\n",
    "from collections import UserDict\n",
    "from common.utils import load_data, mape\n",
    "from IPython.display import Image\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.float_format = '{:,.2f}'.format\n",
    "np.set_printoptions(precision=2)\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the data from csv into a Pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>2,698.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>2,558.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>2,444.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>2,402.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>2,403.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2012-01-01 00:00:00 2,698.00\n",
       "2012-01-01 01:00:00 2,558.00\n",
       "2012-01-01 02:00:00 2,444.00\n",
       "2012-01-01 03:00:00 2,402.00\n",
       "2012-01-01 04:00:00 2,403.00"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "energy = load_data('data')[['load']]\n",
    "energy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create train, validation and test sets\n",
    "\n",
    "We separate our dataset into train, validation and test sets. We train the model on the train set. The validation set is used to evaluate the model after each training epoch and ensure that the model is not overfitting the training data. After the model has finished training, we evaluate the model on the test set. We must ensure that the validation set and test set cover a later period in time from the training set, to ensure that the model does not gain from information from future time periods.\n",
    "\n",
    "We will allocate the period 1st November 2014 to 31st December 2014 to the test set. The period 1st September 2014 to 31st October is allocated to validation set. All other time periods are available for the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_start_dt = '2014-09-01 00:00:00'\n",
    "test_start_dt = '2014-11-01 00:00:00'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'train'}) \\\n",
    "    .join(energy[(energy.index >=valid_start_dt) & (energy.index < test_start_dt)][['load']] \\\n",
    "          .rename(columns={'load':'validation'}), how='outer') \\\n",
    "    .join(energy[test_start_dt:][['load']].rename(columns={'load':'test'}), how='outer') \\\n",
    "    .plot(y=['train', 'validation', 'test'], figsize=(15, 8), fontsize=12)\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - training set\n",
    "\n",
    "For this example, we will set *T=6*. This means that the input for each sample is a vector of the prevous 6 hours of the energy load. The choice of *T=6* was arbitrary but should be selected through experimentation.\n",
    "\n",
    "*HORIZON=1* specifies that we have a forecasting horizon of 1 (*t+1*)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/one_step_forecast.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = 6\n",
    "HORIZON = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data preparation for the training set will involve the following steps:\n",
    "\n",
    "1. Filter the original dataset to include only that time period reserved for the training set\n",
    "2. Scale the time series such that the values fall within the interval (0, 1)\n",
    "3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example\n",
    "4. Discard any samples with missing values\n",
    "5. Transform this Pandas dataframe into a numpy array of shape (samples, features) for input into Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Filter the original dataset to include only that time period reserved for the training set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create training set containing only the model features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = energy.copy()[energy.index < valid_start_dt][['load']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Scale the time series such that the values fall within the interval (0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale data to be in range (0, 1). This transformation should be calibrated on the training set only. This is to prevent information from the validation or test sets leaking into the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2012-01-01 00:00:00  0.22\n",
       "2012-01-01 01:00:00  0.18\n",
       "2012-01-01 02:00:00  0.14\n",
       "2012-01-01 03:00:00  0.13\n",
       "2012-01-01 04:00:00  0.13\n",
       "2012-01-01 05:00:00  0.15\n",
       "2012-01-01 06:00:00  0.18\n",
       "2012-01-01 07:00:00  0.23\n",
       "2012-01-01 08:00:00  0.29\n",
       "2012-01-01 09:00:00  0.35"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler()\n",
    "train['load'] = scaler.fit_transform(train)\n",
    "train.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Original vs scaled data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "energy[energy.index < valid_start_dt][['load']].rename(columns={'load':'original load'}).plot.hist(bins=100, fontsize=12)\n",
    "train.rename(columns={'load':'scaled load'}).plot.hist(bins=100, fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Shift the values of the time series to create a Pandas dataframe containing all the data for a single training example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we create the target (*y_t+1*) variable. If we use the convention that the dataframe is indexed on time *t*, we need to shift the *load* variable forward one hour in time. Using the freq parameter we can tell Pandas that the frequency of the time series is hourly. This ensures the shift does not jump over any missing periods in the time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "      <th>y_t+1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load  y_t+1\n",
       "2012-01-01 00:00:00  0.22   0.18\n",
       "2012-01-01 01:00:00  0.18   0.14\n",
       "2012-01-01 02:00:00  0.14   0.13\n",
       "2012-01-01 03:00:00  0.13   0.13\n",
       "2012-01-01 04:00:00  0.13   0.15\n",
       "2012-01-01 05:00:00  0.15   0.18\n",
       "2012-01-01 06:00:00  0.18   0.23\n",
       "2012-01-01 07:00:00  0.23   0.29\n",
       "2012-01-01 08:00:00  0.29   0.35\n",
       "2012-01-01 09:00:00  0.35   0.37"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted = train.copy()\n",
    "train_shifted['y_t+1'] = train_shifted['load'].shift(-1, freq='H')\n",
    "train_shifted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to shift the load variable back 6 times to create the input sequence:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "for t in range(1, T+1):\n",
    "    train_shifted[str(T-t)] = train_shifted['load'].shift(T-t, freq='H')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 00:00:00</th>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 01:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 02:00:00</th>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 03:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 04:00:00</th>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>nan</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-5  load_t-4  load_t-3  \\\n",
       "2012-01-01 00:00:00           0.22   0.18       nan       nan       nan   \n",
       "2012-01-01 01:00:00           0.18   0.14       nan       nan       nan   \n",
       "2012-01-01 02:00:00           0.14   0.13       nan       nan       nan   \n",
       "2012-01-01 03:00:00           0.13   0.13       nan       nan      0.22   \n",
       "2012-01-01 04:00:00           0.13   0.15       nan      0.22      0.18   \n",
       "2012-01-01 05:00:00           0.15   0.18      0.22      0.18      0.14   \n",
       "2012-01-01 06:00:00           0.18   0.23      0.18      0.14      0.13   \n",
       "2012-01-01 07:00:00           0.23   0.29      0.14      0.13      0.13   \n",
       "2012-01-01 08:00:00           0.29   0.35      0.13      0.13      0.15   \n",
       "2012-01-01 09:00:00           0.35   0.37      0.13      0.15      0.18   \n",
       "\n",
       "                     load_t-2  load_t-1  load_t  \n",
       "2012-01-01 00:00:00       nan       nan    0.22  \n",
       "2012-01-01 01:00:00       nan      0.22    0.18  \n",
       "2012-01-01 02:00:00      0.22      0.18    0.14  \n",
       "2012-01-01 03:00:00      0.18      0.14    0.13  \n",
       "2012-01-01 04:00:00      0.14      0.13    0.13  \n",
       "2012-01-01 05:00:00      0.13      0.13    0.15  \n",
       "2012-01-01 06:00:00      0.13      0.15    0.18  \n",
       "2012-01-01 07:00:00      0.15      0.18    0.23  \n",
       "2012-01-01 08:00:00      0.18      0.23    0.29  \n",
       "2012-01-01 09:00:00      0.23      0.29    0.35  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_col = 'y_t+1'\n",
    "X_cols = ['load_t-5',\n",
    "          'load_t-4',\n",
    "          'load_t-3',\n",
    "          'load_t-2',\n",
    "          'load_t-1',\n",
    "          'load_t']\n",
    "train_shifted.columns = ['load_original']+[y_col]+X_cols\n",
    "train_shifted.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Discard any samples with missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how we have missing values for the input sequences for the first 5 samples. We will discard these:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 08:00:00</th>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 09:00:00</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-5  load_t-4  load_t-3  \\\n",
       "2012-01-01 05:00:00           0.15   0.18      0.22      0.18      0.14   \n",
       "2012-01-01 06:00:00           0.18   0.23      0.18      0.14      0.13   \n",
       "2012-01-01 07:00:00           0.23   0.29      0.14      0.13      0.13   \n",
       "2012-01-01 08:00:00           0.29   0.35      0.13      0.13      0.15   \n",
       "2012-01-01 09:00:00           0.35   0.37      0.13      0.15      0.18   \n",
       "\n",
       "                     load_t-2  load_t-1  load_t  \n",
       "2012-01-01 05:00:00      0.13      0.13    0.15  \n",
       "2012-01-01 06:00:00      0.13      0.15    0.18  \n",
       "2012-01-01 07:00:00      0.15      0.18    0.23  \n",
       "2012-01-01 08:00:00      0.18      0.23    0.29  \n",
       "2012-01-01 09:00:00      0.23      0.29    0.35  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted = train_shifted.dropna(how='any')\n",
    "train_shifted.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Transform into a numpy arrays of shapes (samples, features) and (samples,1) for input into Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now convert the target and input features into numpy arrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train_shifted[[y_col]].as_matrix()\n",
    "X_train = train_shifted[X_cols].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a vector for target variable of shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23370, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target varaible for the first 3 samples looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.18],\n",
       "       [0.23],\n",
       "       [0.29]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tensor for the input features now has the shape:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23370, 6)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the first 3 samples looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22, 0.18, 0.14, 0.13, 0.13, 0.15],\n",
       "       [0.18, 0.14, 0.13, 0.13, 0.15, 0.18],\n",
       "       [0.14, 0.13, 0.13, 0.15, 0.18, 0.23]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sense check this against the first 3 records of the original dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>load_original</th>\n",
       "      <th>y_t+1</th>\n",
       "      <th>load_t-5</th>\n",
       "      <th>load_t-4</th>\n",
       "      <th>load_t-3</th>\n",
       "      <th>load_t-2</th>\n",
       "      <th>load_t-1</th>\n",
       "      <th>load_t</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2012-01-01 05:00:00</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 06:00:00</th>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01 07:00:00</th>\n",
       "      <td>0.23</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load_original  y_t+1  load_t-5  load_t-4  load_t-3  \\\n",
       "2012-01-01 05:00:00           0.15   0.18      0.22      0.18      0.14   \n",
       "2012-01-01 06:00:00           0.18   0.23      0.18      0.14      0.13   \n",
       "2012-01-01 07:00:00           0.23   0.29      0.14      0.13      0.13   \n",
       "\n",
       "                     load_t-2  load_t-1  load_t  \n",
       "2012-01-01 05:00:00      0.13      0.13    0.15  \n",
       "2012-01-01 06:00:00      0.13      0.15    0.18  \n",
       "2012-01-01 07:00:00      0.15      0.18    0.23  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_shifted.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preparation - validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we follow a similar process for the validation set. We keep *T* hours from the training set in order to construct initial features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-31 19:00:00</th>\n",
       "      <td>3,969.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 20:00:00</th>\n",
       "      <td>3,869.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 21:00:00</th>\n",
       "      <td>3,643.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 22:00:00</th>\n",
       "      <td>3,365.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 23:00:00</th>\n",
       "      <td>3,097.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2014-08-31 19:00:00 3,969.00\n",
       "2014-08-31 20:00:00 3,869.00\n",
       "2014-08-31 21:00:00 3,643.00\n",
       "2014-08-31 22:00:00 3,365.00\n",
       "2014-08-31 23:00:00 3,097.00"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(valid_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "valid = energy.copy()[(energy.index >=look_back_dt) & (energy.index < test_start_dt)][['load']]\n",
    "valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the series using the transformer fitted on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-08-31 19:00:00</th>\n",
       "      <td>0.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 20:00:00</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 21:00:00</th>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 22:00:00</th>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-08-31 23:00:00</th>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-08-31 19:00:00  0.61\n",
       "2014-08-31 20:00:00  0.58\n",
       "2014-08-31 21:00:00  0.51\n",
       "2014-08-31 22:00:00  0.43\n",
       "2014-08-31 23:00:00  0.34"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid['load'] = scaler.transform(valid)\n",
    "valid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare validation inputs in the same way as the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_shifted = valid.copy()\n",
    "valid_shifted['y+1'] = valid_shifted['load'].shift(-1, freq='H')\n",
    "for t in range(1, T+1):\n",
    "    valid_shifted['load_t-'+str(T-t)] = valid_shifted['load'].shift(T-t, freq='H')\n",
    "valid_shifted = valid_shifted.dropna(how='any')\n",
    "y_valid = valid_shifted['y+1'].as_matrix()\n",
    "X_valid = valid_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_valid.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1463, 6)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_valid.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implement Feedforward Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We implement feed-forward neural network with the 6 inputs, 5 neurons in hidden layer and one neuron in output layer:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/ff_one_step_univariate.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras import regularizers\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "LATENT_DIM = 5 # number of units in the dense layer\n",
    "BATCH_SIZE = 32 # number of samples per mini-batch\n",
    "EPOCHS = 50 # maximum number of times the training algorithm will cycle through all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda/envs/py35/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(LATENT_DIM, activation=\"relu\", input_shape=(T,)))\n",
    "model.add(Dense(HORIZON))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use RMSprop optimizer and mean squared error as the loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='RMSprop', loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 5)                 35        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 6         \n",
      "=================================================================\n",
      "Total params: 41\n",
      "Trainable params: 41\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Early stopping trick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image('./images/early_stopping.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify the early stopping criteria. We **monitor** the validation loss (in this case the mean squared error) on the validation set after each training epoch. If the validation loss has not improved by **min_delta** after **patience** epochs, we stop the training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_val = ModelCheckpoint('model_{epoch:02d}.h5', save_best_only=True, mode='min', period=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /anaconda/envs/py35/lib/python3.5/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 23370 samples, validate on 1463 samples\n",
      "Epoch 1/50\n",
      "23370/23370 [==============================] - 18s 763us/step - loss: 0.0148 - val_loss: 0.0031\n",
      "Epoch 2/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0030 - val_loss: 0.0025\n",
      "Epoch 3/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0023 - val_loss: 0.0017\n",
      "Epoch 4/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0020 - val_loss: 0.0016\n",
      "Epoch 5/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0018 - val_loss: 0.0014\n",
      "Epoch 6/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0017 - val_loss: 0.0013\n",
      "Epoch 7/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0016 - val_loss: 0.0014\n",
      "Epoch 8/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0015 - val_loss: 0.0012\n",
      "Epoch 9/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0014 - val_loss: 0.0011\n",
      "Epoch 10/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0013 - val_loss: 0.0010\n",
      "Epoch 11/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0013 - val_loss: 0.0011\n",
      "Epoch 12/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0012 - val_loss: 9.7521e-04\n",
      "Epoch 13/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0012 - val_loss: 0.0012\n",
      "Epoch 14/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0011 - val_loss: 8.4618e-04\n",
      "Epoch 15/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 0.0011 - val_loss: 7.9154e-04\n",
      "Epoch 16/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 0.0010 - val_loss: 0.0011\n",
      "Epoch 17/50\n",
      "23370/23370 [==============================] - 1s 52us/step - loss: 0.0010 - val_loss: 7.3906e-04\n",
      "Epoch 18/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 9.7352e-04 - val_loss: 7.6310e-04\n",
      "Epoch 19/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 9.3998e-04 - val_loss: 7.2419e-04\n",
      "Epoch 20/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 9.0475e-04 - val_loss: 8.7259e-04\n",
      "Epoch 21/50\n",
      "23370/23370 [==============================] - 1s 52us/step - loss: 8.7435e-04 - val_loss: 0.0012\n",
      "Epoch 22/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 8.5570e-04 - val_loss: 6.1370e-04\n",
      "Epoch 23/50\n",
      "23370/23370 [==============================] - 1s 52us/step - loss: 8.3050e-04 - val_loss: 6.7139e-04\n",
      "Epoch 24/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 8.0766e-04 - val_loss: 6.0097e-04\n",
      "Epoch 25/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 7.8751e-04 - val_loss: 5.9429e-04\n",
      "Epoch 26/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 7.6967e-04 - val_loss: 5.6846e-04\n",
      "Epoch 27/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 7.5260e-04 - val_loss: 7.3220e-04\n",
      "Epoch 28/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 7.4245e-04 - val_loss: 5.3642e-04\n",
      "Epoch 29/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 7.2500e-04 - val_loss: 5.3738e-04\n",
      "Epoch 30/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 7.1388e-04 - val_loss: 5.2011e-04\n",
      "Epoch 31/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 7.0160e-04 - val_loss: 5.1937e-04\n",
      "Epoch 32/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.9288e-04 - val_loss: 5.7761e-04\n",
      "Epoch 33/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.8045e-04 - val_loss: 5.1441e-04\n",
      "Epoch 34/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.7527e-04 - val_loss: 5.3415e-04\n",
      "Epoch 35/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.6796e-04 - val_loss: 7.6080e-04\n",
      "Epoch 36/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.6083e-04 - val_loss: 5.2191e-04\n",
      "Epoch 37/50\n",
      "23370/23370 [==============================] - 1s 52us/step - loss: 6.5505e-04 - val_loss: 4.8295e-04\n",
      "Epoch 38/50\n",
      "23370/23370 [==============================] - 1s 54us/step - loss: 6.4862e-04 - val_loss: 6.1764e-04\n",
      "Epoch 39/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.3967e-04 - val_loss: 4.9077e-04\n",
      "Epoch 40/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.3830e-04 - val_loss: 4.6722e-04\n",
      "Epoch 41/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.3614e-04 - val_loss: 4.9257e-04\n",
      "Epoch 42/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.3261e-04 - val_loss: 5.3336e-04\n",
      "Epoch 43/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.2496e-04 - val_loss: 6.1233e-04\n",
      "Epoch 44/50\n",
      "23370/23370 [==============================] - 1s 52us/step - loss: 6.2531e-04 - val_loss: 5.9952e-04\n",
      "Epoch 45/50\n",
      "23370/23370 [==============================] - 1s 53us/step - loss: 6.1947e-04 - val_loss: 4.8225e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,\n",
    "                    y_train,\n",
    "                    batch_size=BATCH_SIZE,\n",
    "                    epochs=EPOCHS,\n",
    "                    validation_data=(X_valid, y_valid),\n",
    "                    callbacks=[earlystop, best_val],\n",
    "                    verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the model with the smallest mape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_epoch = np.argmin(np.array(history.history['val_loss']))+1\n",
    "model.load_weights(\"model_{:02d}.h5\".format(best_epoch))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "plot training and validation losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = pd.DataFrame.from_dict({'train_loss':history.history['loss'], 'val_loss':history.history['val_loss']})\n",
    "plot_df.plot(logy=True, figsize=(10,10), fontsize=12)\n",
    "plt.xlabel('epoch', fontsize=12)\n",
    "plt.ylabel('loss', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>2,514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>2,434.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>2,390.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>2,382.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>2,419.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        load\n",
       "2014-11-01 00:00:00 2,514.00\n",
       "2014-11-01 01:00:00 2,434.00\n",
       "2014-11-01 02:00:00 2,390.00\n",
       "2014-11-01 03:00:00 2,382.00\n",
       "2014-11-01 04:00:00 2,419.00"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "look_back_dt = dt.datetime.strptime(test_start_dt, '%Y-%m-%d %H:%M:%S') - dt.timedelta(hours=T-1)\n",
    "test = energy.copy()[test_start_dt:][['load']]\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scale the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>load</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-01 00:00:00</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 01:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 02:00:00</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 03:00:00</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01 04:00:00</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     load\n",
       "2014-11-01 00:00:00  0.16\n",
       "2014-11-01 01:00:00  0.14\n",
       "2014-11-01 02:00:00  0.13\n",
       "2014-11-01 03:00:00  0.12\n",
       "2014-11-01 04:00:00  0.14"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test['load'] = scaler.transform(test)\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create test set features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_shifted = test.copy()\n",
    "test_shifted['y_t+1'] = test_shifted['load'].shift(-1, freq='H')\n",
    "for t in range(1, T+1):\n",
    "    test_shifted['load_t-'+str(T-t)] = test_shifted['load'].shift(T-t, freq='H')\n",
    "test_shifted = test_shifted.dropna(how='any')\n",
    "y_test = test_shifted['y_t+1'].as_matrix()\n",
    "X_test = test_shifted[['load_t-'+str(T-t) for t in range(1, T+1)]].as_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make predictions on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22],\n",
       "       [0.3 ],\n",
       "       [0.38],\n",
       "       ...,\n",
       "       [0.53],\n",
       "       [0.45],\n",
       "       [0.41]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compare predictions to actual load"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>h</th>\n",
       "      <th>prediction</th>\n",
       "      <th>actual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-11-01 05:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,687.07</td>\n",
       "      <td>2,714.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-11-01 06:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>2,954.29</td>\n",
       "      <td>2,970.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-11-01 07:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,195.96</td>\n",
       "      <td>3,189.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-11-01 08:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,336.52</td>\n",
       "      <td>3,356.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-11-01 09:00:00</td>\n",
       "      <td>t+1</td>\n",
       "      <td>3,471.93</td>\n",
       "      <td>3,436.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp    h  prediction   actual\n",
       "0 2014-11-01 05:00:00  t+1    2,687.07 2,714.00\n",
       "1 2014-11-01 06:00:00  t+1    2,954.29 2,970.00\n",
       "2 2014-11-01 07:00:00  t+1    3,195.96 3,189.00\n",
       "3 2014-11-01 08:00:00  t+1    3,336.52 3,356.00\n",
       "4 2014-11-01 09:00:00  t+1    3,471.93 3,436.00"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval_df = pd.DataFrame(predictions, columns=['t+'+str(t) for t in range(1, HORIZON+1)])\n",
    "eval_df['timestamp'] = test_shifted.index\n",
    "eval_df = pd.melt(eval_df, id_vars='timestamp', value_name='prediction', var_name='h')\n",
    "eval_df['actual'] = np.transpose(y_test).ravel()\n",
    "eval_df[['prediction', 'actual']] = scaler.inverse_transform(eval_df[['prediction', 'actual']])\n",
    "eval_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean absolute percentage error over all predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016553609590006874"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mape(eval_df['prediction'], eval_df['actual'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the predictions vs the actuals for the first week of the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_df[eval_df.timestamp<'2014-11-08'].plot(x='timestamp', y=['prediction', 'actual'], style=['r', 'b'], figsize=(15, 8))\n",
    "plt.xlabel('timestamp', fontsize=12)\n",
    "plt.ylabel('load', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "clean up model files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "for m in glob('model_*.h5'):\n",
    "    os.remove(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
